Space-time adaptive processing (STAP) is frequently used in radar systems to detect a target, e.g., a car, or a plane. STAP has been known since the early 1970's. In airborne radar systems, STAP improves target detection when interference in an environment, e.g., ground clutter and jamming, is a problem. STAP can achieve order-of-magnitude sensitivity improvements in target detection.
Typically, STAP involves a two-dimensional filtering technique applied to signals acquired by a phased-array antenna with multiple spatial channels. Generally, the STAP is a combination of the multiple spatial channels with time dependent pulse-Doppler waveforms. By applying statistics of interference of the environment, a space-time adaptive weight vector is formed. Then, the weight vector is applied to the coherent signals received by the radar to detect the target.
A number of non-adaptive and adaptive STAP detectors are available for detecting moving targets in non-Gaussian distributed environments. Due to the additional time-correlated texture component, the optimum detection in the compound-Gaussian yields an implicit form, in most cases. The solution to the optimum detector usually resorts to an expectation-maximization procedure. On the other hand, sub-optimal detectors in the compound-Gaussian case are expressed in closed-form. Among these detectors are the normalized adaptive matched filter (NAMF) with the standard sample covariance matrix, and the NAMF with the normalized sample covariance matrix.
Speckle in a compound-Gaussian distributed environment has a low-rank structure. A speckle pattern is a random intensity pattern produced by mutual interference of a set of wavefronts. Therefore, an adaptive eigen value/singular-value decomposition (EVD/SVD) is used, where, instead of using the inverse of the sample covariance matrix, a projection of the received signal and steering vector into the null space of the clutter subspace is used to obtain the detection statistics. The EVD/SVD—based method is able to reduce the training requirement to O(2r), where r is the rank of the disturbance covariance matrix. However, the computational complexity of this method remains high as O(M3N3), where M is the number of spatial channels and N is the number of pulses. If MN becomes large, then the high computational complexity of the EVD/SVD—based methods are impractical for real-time applications.
FIG. 1 shows a block diagram of the conventional STAP method. When no target is detected, acquired signals 101 include a test signal x0 110 and a set of training signals Xk k=1, 2, . . . , K, 120, wherein K is a total number of training signals, which are independent and identically distributed (i.i.d.). The target signal can be expressed as a product of a known steering vector s 130 and unknown amplitude α.
That method normalizes 140 the training signals xk 120, and then computes the normalized sample covariance matrix 150 using the normalized training data 140. Then, eigenvalue decomposition 160 is applied to the normalized sample covariance matrix 150 to produce a matrix U 165 representing the clutter subspace. Next, the method determines a test statistics 170 describing a likelihood of presence of the target in a test signal 110 as shown in (1).
                                          T            prior_art                    =                                    |                                                                    s                    H                                    ⁡                                      (                                          I                      -                                              UU                        H                                                              )                                                  ⁢                                  x                  0                                            ⁢                              |                2                                                                    (                                                                            s                      H                                        ⁡                                          (                                              I                        -                                                  UU                          H                                                                    )                                                        ⁢                  s                                )                            ⁢                              (                                                                            x                      0                      H                                        ⁡                                          (                                              I                        -                                                  UU                          H                                                                    )                                                        ⁢                                      x                    o                    H                                                  )                                                    ,                            (        1        )            where s is a known steering vector for a particular Doppler frequency and angle of arrival, I is an identity matrix, x0 is the data vector to be tested for target presence, and H is the Hermitian transpose operation.
The resulting test statistic Tprior—art 170 is compared to a threshold 180 to detect 190 whether a target is present, or not
The EVD/SVD based STAP method works well for compound-Gaussian distributed, i.e., non-homogeneous environments. However, this method is computationally expensive. Accordingly there is a need in the art to provide a low complexity STAP method for detecting a target in non-homogeneous environments